Exudate detection for diabetic retinopathy with convolutional neural networks

Abstract

Exudate detection is an essential task for computer-aid diagnosis of diabetic retinopathy (DR), so as to monitor the progress of DR. In this paper, deep convolutional neural network (CNN) is adopted to achieve pixel-wise exudate identification. The CNN model is first trained with expert labeled exudates image patches and then saved as off-line classifier. In order to achieve pixel-level accuracy meanwhile reduce computational time, potential exudate candidate points are first extracted with morphological ultimate opening algorithm. Then the local region (64 × 64) surrounding the candidate points are forwarded to the trained CNN model for classification/identification. A pixel-wise accuracy of 91.92%, sensitivity of 88.85% and specificity of 96% is achieved with the proposed CNN architecture on the test database.